Schizophrenia is highly heritable and the cause of substantial morbidity, mortality, and personal and societal costs. To increase our understanding of the biological basis of schizophrenia, it is essential to identify precise causal mutations that influence risk. GWAS have been remarkably successful in identifying genomic regions harboring common SNPs associated with schizophrenia. The most recent PGC analysis yielded 128 genome- wide significant loci and this number will likely increase. However, the causative variants underlying nearly all GWAS loci have proven elusive. Existing imputation resources are not optimized for GWAS loci. The overarching goal of this proposal is to identify causative mutations within schizophrenia GWAS loci. Common risk loci resulting from changes in copy number (i.e., copy number polymorphism, or CNP) are attractive casual mutations. CNPs that reside within GWAS loci (i.e., tagged by the associated SNPs) can alter the dosage or structure of regulatory elements and genes and exert functional impact to drive the observed association with SCZ. Multiple examples are in the literature. The contribution of CNPs to schizophrenia is unknown because large-scale surveys of CNPs are infrequent. CNPs are inaccessible to most current methods, and CNP imputation methods are underdeveloped. Since CNPs are highly plausible but largely unexplored, we propose to deeply sequence schizophrenia samples at the PGC GWAS loci to identify CNPs and uncommon SNP/indels, and then impute them into very large samples to test for association with very high power with schizophrenia. Identifying uncommon SNP/indels maximizes expenditure of the proposed study. Successful completion of the proposed work will enhance our understanding of biological mechanisms underlying the etiology of schizophrenia. If we can identify even a few CNPs or uncommon SNP/indels altering schizophrenia risk, it will represent an important advance in the field. Any CNP association is likely to have immediate biological relevance and amenability to current molecular biology and neuroscience methods. Furthermore, these schizophrenia-associated GWAS loci have never been deeply sequenced in the world-literature. The targeted resequencing data, the imputation reference, and CNP imputation methods generated from this R01 will be useful resources for the human genetics community. These data will also ensure that the community's forthcoming functional work on these risk loci is focused on the most promising variants.